bioRxiv : the preprint server for biology最新文献

The cotranslational misfolding of the cystic fibrosis transmembrane conductance regulator chloride channel (CFTR) plays a central role in the molecular basis of cystic fibrosis (CF). The misfolding of the most common CF variant (ΔF508) remodels both the translational regulation and quality control of CFTR. Nevertheless, it is unclear how the misassembly of the nascent polypeptide may directly influence the activity of the translation machinery. In this work, we identify a structural motif within the CFTR transcript that stimulates efficient -1 ribosomal frameshifting and triggers the premature termination of translation. Though this motif does not appear to impact the interactome of wild-type CFTR, silent mutations that disrupt this RNA structure alter the association of nascent ΔF508 CFTR with numerous translation and quality control proteins. Moreover, disrupting this RNA structure enhances the functional gating of the ΔF508 CFTR channel at the plasma membrane and its pharmacological rescue by the CFTR modulators contained in the CF drug Trikafta. The effects of the RNA structure on ΔF508 CFTR appear to be attenuated in the absence of the ER membrane protein complex (EMC), which was previously found to modulate ribosome collisions during "preemptive quality control" of a misfolded CFTR homolog. Together, our results reveal that ribosomal frameshifting selectively modulates the assembly, function, and pharmacological rescue of a misfolded CFTR variant. These findings suggest interactions between the nascent chain, quality control machinery, and ribosome may dynamically modulate ribosomal frameshifting in order to tune the processivity of translation in response to cotranslational misfolding.

Skeletal stem cells have been isolated from various tissues, including periosteum and bone marrow, where they exhibit key functions in bone biology and hematopoiesis, respectively. The role of periosteal skeletal stem cells in bone regeneration and healing has been extensively studied, but their ability to contribute to the bone marrow stroma is still under debate. In the present study, we characterized a whole bone transplantation model that mimics the initial bone marrow necrosis and fatty infiltration seen after injury. Using this model and a lineage tracing approach, we observed the migration of periosteal skeletal stem cells into the bone marrow after transplantation. Once in the bone marrow, periosteal skeletal stem cells are phenotypically and functionally reprogrammed into bone marrow mesenchymal stem cells that express high levels of hematopoietic stem cell niche factors such as Cxcl12 and Kitl. In addition, using in-vitro and in-vivo approaches, we found that periosteal skeletal stem cells are more resistant to acute stress than bone marrow mesenchymal stem cells. These results highlight the plasticity of periosteal skeletal stem cells and their potential role in bone marrow regeneration after bone marrow injury.

The ability to detect and quantify microbiota over time has a plethora of clinical, basic science, and public health applications. One of the primary means of tracking microbiota is through sequencing technologies. When the microorganism of interest is well characterized or known a priori , targeted sequencing is often used. In many applications, however, untargeted bulk (shotgun) sequencing is more appropriate; for instance, the tracking of infection transmission events and nucleotide variants across multiple genomic loci, or studying the role of multiple genes in a particular phenotype. Given these applications, and the observation that pathogens (e.g. Clostridioides difficile, Escherichia coli, Salmonella enterica ) and other taxa of interest can reside at low relative abundance in the gastrointestinal tract, there is a critical need for algorithms that accurately track low-abundance taxa with strain level resolution. Here we present a sequence quality- and time-aware model, ChronoStrain , that introduces uncertainty quantification to gauge low-abundance species and significantly outperforms the current state-of-the-art on both real and synthetic data. ChronoStrain leverages sequences' quality scores and the samples' temporal information to produce a probability distribution over abundance trajectories for each strain tracked in the model. We demonstrate Chronostrain's improved performance in capturing post-antibiotic Escherichia coli strain blooms among women with recurrent urinary tract infections (UTIs) from the UTI Microbiome (UMB) Project. Other strain tracking models on the same data either show inconsistent temporal colonization or can only track consistently using very coarse groupings. In contrast, our probabilistic outputs can reveal the relationship between low-confidence strains present in the sample that cannot be reliably assigned a single reference label (either due to poor coverage or novelty) while simultaneously calling high-confidence strains that can be unambiguously assigned a label. We also analyze samples from the Early Life Microbiota Colonisation (ELMC) Study demonstrating the algorithm's ability to correctly identify Enterococcus faecalis strains using paired sample isolates as validation.

False discovery is an ever-present concern in omics research, especially for burgeoning technologies with unvetted specificity of their biomolecular measurements, as such unknowns obscure the ability to characterize biologically informative features from studies performed with any single platform. Accordingly, performing replication studies of the same samples using different omics platforms is a viable strategy for identifying high-confidence molecular associations that are conserved across studies. However, an important caveat of replication studies that include the same samples is that they are inherently non-independent, leading to overestimating conservation if studies are treated otherwise. Strategies for accounting for such inter-study dependencies have been proposed for meta-analysis methods devised to increase statistical power to detect molecular associations in one or more studies. Still, they are not immediately suited for identifying conserved molecular associations across multiple studies. Here, we present a unifying strategy for performing inter-study conservation analysis as an alternative to meta-analysis strategies for aggregating summary statistical results of shared features across complementary studies while accounting for inter-study dependency. This method, which we call "adjusted maximum p-value" (AdjMaxP), is easy to implement with inter-study dependency and conservation estimated directly from the p-values from each study's molecular feature-level association testing results. Through simulation-based assessment, we demonstrate AdjMaxP's improved performance for accurately identifying conserved features over a related meta-analysis strategy for non-independent studies. AdjMaxP offers an easily implementable strategy for improving the precision of analyses for biomarker discovery from cross-platform omics study designs, thereby facilitating the adoption of such protocols for robust inference from emerging omics technologies.

Developmental dyslexia is typically associated with difficulties in basic auditory processing and in manipulating speech sounds. However, the neuroanatomical correlates of auditory difficulties in developmental dyslexia (DD) and their contribution to individual clinical phenotypes are still unknown. Recent intracranial electrocorticography findings associated processing of sound amplitude rises and speech sounds with posterior and middle superior temporal gyrus (STG), respectively. We hypothesize that regional STG anatomy will relate to specific auditory abilities in DD, and that auditory processing abilities will relate to behavioral difficulties with speech and reading. One hundred and ten children (78 DD, 32 typically developing, age 7-15 years) completed amplitude rise time and speech in noise discrimination tasks. They also underwent a battery of cognitive tests. Anatomical MRI scans were used to identify regions in which local cortical gyrification complexity correlated with auditory behavior. Behaviorally, amplitude rise time but not speech in noise performance was impaired in DD. Neurally, amplitude rise time and speech in noise performance correlated with gyrification in posterior and middle STG, respectively. Furthermore, amplitude rise time significantly contributed to reading impairments in DD, while speech in noise only explained variance in phonological awareness. Finally, amplitude rise time and speech in noise performance were not correlated, and each task was correlated with distinct neuropsychological measures, emphasizing their unique contributions to DD. Overall, we provide a direct link between the neurodevelopment of the left STG and individual variability in auditory processing abilities in neurotypical and dyslexic populations.

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins via three-dimensional structures of target proteins, but as a result, struggle to design binders to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model, and subsequently screen these novel linear sequences for target-selective interaction activity via a CLIP-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly-ranked, target-specific peptides experimentally, both as inhibitory peptides and as fusions to E3 ubiquitin ligase domains, demonstrating functionally potent binding and degradation of conformationally diverse protein targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding linear peptides to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.

Relatively little is known about the way vision is used to guide locomotion in the natural world. What visual features are used to choose paths in natural complex terrain? To answer this question, we measured eye and body movements while participants walked in natural outdoor environments. We incorporated measurements of the 3D terrain structure into our analyses and reconstructed the terrain along the walker's path, applying photogrammetry techniques to the eyetracker's scene camera videos. Combining these reconstructions with the walker's body movements, we demonstrate that walkers take terrain structure into account when selecting paths through an environment. We find that they change direction to avoid taking steeper steps that involve large height changes, instead of choosing more circuitous, relatively flat paths. Our data suggest walkers plan the location of individual footholds and plan ahead to select flatter paths. These results provide evidence that locomotor behavior in natural environments is controlled by decision mechanisms that account for multiple factors, including sensory and motor information, costs, and path planning.

Mutations in the GBA1 gene have been identified as a prevalent genetic risk factor for Parkinson's disease (PD). GBA1 mutations impair enzymatic activity, leading to lysosomal dysfunction and elevated levels of α-synuclein (α-syn). While most research has primarily focused on GBA1's role in promoting synucleinopathy, emerging evidence suggests that neuroinflammation may be a key pathogenic alteration caused by GBA1 deficiency. To examine the molecular mechanism underlying GBA1 deficiency-mediated neuroinflammation, we generated Gba1 E326K knock-in (KI) mice using the CRISPR/Cas9 technology, which is linked to an increased risk of PD and dementia with Lewy bodies (DLB). In the ventral midbrain and hippocampus of 24-month-old Gba1 E326K KI mice, we found a moderate decline in GBA1 enzymatic activity, a buildup of glucosylceramide, and an increase in microglia density. Furthermore, we observed increased levels of pro-inflammatory cytokines and formation of reactive astrocytes in primary microglia and astrocytes, respectively, cultured from Gba1 E326K KI mice following treatment with pathologic α-syn preformed fibrils (PFF). Additionally, the gut inoculation of α-syn PFF in Gba1 E326K KI mice significantly enhanced the accumulation of Lewy bodies in the dentate gyrus of the hippocampus, accompanied by aggravated neuroinflammation and exacerbated non-motor symptoms. This research significantly enhances our understanding of the Gba1 E326K mutation's involvement in neuroinflammation and the cell-to-cell transmission of pathogenic α-syn in the brain, thereby opening new therapeutic avenues.

Adolescence is a period of increased risk taking, including increased alcohol and drug use. Multiple clinical studies report a positive relationship between adolescent alcohol consumption and risk of developing an alcohol use disorder (AUD) in adulthood. However, few preclinical studies have attempted to tease apart the biological contributions of adolescent alcohol exposure, independent of other social, environmental, and stress factors, and studies that have been conducted show mixed results. Here we use several adolescent voluntary consumption of alcohol models, conducted across four labs in three institutes and with two rodent species, to investigate the ramifications of adolescent alcohol consumption on adulthood alcohol consumption in controlled, pre-clinical environments. We consistently demonstrate a lack of robust increases in adulthood alcohol consumption. This work highlights that risks seen in both human datasets and other murine drinking models may be due to unique social and environmental factors - some of which may be unique to humans.

A key goal of evolutionary genomics is to harness molecular data to draw inferences about selective forces that have acted on genomes. The field progresses in large part through the development of advanced molecular-evolution analysis methods. Here we explored the intersection between classical sequence-based tests for selection and an empirical expression-based approach, using stem cells from Mus musculus subspecies as a model. Using a test of directional, cis -regulatory evolution across genes in pathways, we discovered a unique program of induction of translation genes in stem cells of the Southeast Asian mouse M. m. castaneus relative to its sister taxa. We then mined population-genomic sequences to pursue underlying regulatory mechanisms for this expression divergence, finding robust evidence for alleles unique to M. m. castaneus at the upstream regions of the translation genes. We interpret our data under a model of changes in lineage-specific pressures across Mus musculus in stem cells with high translational capacity. Our findings underscore the rigor of integrating expression and sequence-based methods to generate hypotheses about evolutionary events from long ago.